93,099 research outputs found
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Reasoning is a crucial part of natural language argumentation. To comprehend
an argument, one must analyze its warrant, which explains why its claim follows
from its premises. As arguments are highly contextualized, warrants are usually
presupposed and left implicit. Thus, the comprehension does not only require
language understanding and logic skills, but also depends on common sense. In
this paper we develop a methodology for reconstructing warrants systematically.
We operationalize it in a scalable crowdsourcing process, resulting in a freely
licensed dataset with warrants for 2k authentic arguments from news comments.
On this basis, we present a new challenging task, the argument reasoning
comprehension task. Given an argument with a claim and a premise, the goal is
to choose the correct implicit warrant from two options. Both warrants are
plausible and lexically close, but lead to contradicting claims. A solution to
this task will define a substantial step towards automatic warrant
reconstruction. However, experiments with several neural attention and language
models reveal that current approaches do not suffice.Comment: Accepted as NAACL 2018 Long Paper; see details on the front pag
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning
Comprehension task of SemEval 2018. It is a simple neural network consisting of
three parts, collectively judging whether the logic built on a set of given
sentences (a claim, reason, and warrant) is plausible or not. The model
utilizes contextualized word vectors pre-trained on large machine translation
(MT) datasets as a form of transfer learning, which can help to mitigate the
lack of training data. Quantitative analysis shows that simply leveraging LSTMs
trained on MT datasets outperforms several baselines and non-transferred
models, achieving accuracies of about 70% on the development set and about 60%
on the test set.Comment: SemEval 201
Use or misuse of the selection task? Rejoinder to Fiddick, Cosmides and Tooby
Sperber, Cara, and Girotto (1995) argued that, in Wason's selection task, relevance-guided comprehension processes tend to determine participants' performance and pre-empt the use of other inferential capacities. Because of this, the value of the selection task as a tool for studying human inference has been grossly overestimated. Fiddick, Cosmides, and Tooby (2000) argued against Sperber et al. that specialized inferential mechanisms, in particular the “social contract algorithm” hypothesized by Cosmides (1989), pre-empt more general comprehension abilities, making the selection task a useful tool after all. We rebut this argument. We argue and illustrate with two new experiments, that Fiddick et al. mix the true Wason selection task with a trivially simple categorization task superficially similar to the Wason task, yielding methodologically flawed evidence. We conclude that the extensive use of various kinds of selection tasks in the psychology of reasoning has been quite counter-productive and should be discontinued
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
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